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100% Pass PMI - CPMAI_v7 - Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam–Valid Reliable Test Objectives

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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

Topic 1

Machine Learning: This section is aimed at the Data
AI Lead and addresses practical machine learning applications. It begins with classification, clustering, and reinforcement algorithms, including ensemble methods and evaluation against business needs. Afterwards, it examines neural network architecture design and deep learning implementation across multiple problem types. Generative AI and LLMs follow, covering use-case suitability, limitations, operation explanations, prompt engineering, fine-tuning, and integrating these technologies into augmented intelligence solutions.

Topic 2

Data for AI: This domain targets the Data
AI Lead and explores the central role of data in AI deployments, including Big Data concepts and unstructured data utility. It defines data governance strategies such as steering, stewardship, lifecycle mapping, lineage tracking, and master data practices.

Topic 3

CPMAI Methodology: This domain measures the skills of a Project Manager and outlines the distinctive characteristics of AI projects compared to traditional software development. It investigates failure drivers, ROI justification, data quantity and quality challenges, proof-of-concept issues, real-world deployment barriers, lifecycle continuity, vendor mismatches, stakeholder misalignment, and adaptation of waterfall, lean, and agile approaches through the six phases of the CPMAI framework.

PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
Your team is ready to operationalize the model they have been working on. It's a model that is meant to be used on an "edge device," specifically a mobile phone, and the user may sometimes be in remote locations without regular access to the internet.
What's the most important thing to consider here?

A. Make sure the model lives on the edge device so it can be used regardless of internet connection
B. Make sure the model is available over a cloud-based API
C. Make sure that you can use Generative AI solutions on an edge device
D. Make sure the model lives in a hybrid environment

Answer: A

Explanation:
In the Model Operationalization phase (Phase VI), when targeting edge devices, teams must capture the specific constraints around connectivity and on-device performance. The CPMAI Workbook's Edge Model Data Needs task group instructs project teams to list all requirements and constraints for running models on edge systems-including constraints on external data access-and to determine how models will be deployed locally so they function offline when connectivity is unavailable.
Under "Edge constraints," the Workbook requires teams to "List constraints on model usage including ...
requirements for model result response time, constraints on external data access," underscoring that reliance on a remote API is infeasible without connectivity.
Consequently, the critical operationalization decision is to deploy the model directly on the edge device, ensuring it remains fully functional regardless of internet availability.
=========

NEW QUESTION # 89
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?

A. Used for Hyperpersonalization
B. As an autonomous system removing the human from the loop
C. As a programmatic approach for automation
D. As an Augmented Intelligence system

Answer: D

Explanation:
The CPMAI Glossary defines Augmented Intelligence as "enhancing human abilities with AI," where AI outputs are leveraged by humans to improve decision-making or productivity. Using Generative AI to accelerate or improve your own work is precisely an Augmented Intelligence use case, distinct from full autonomy or simple automation .
=========

NEW QUESTION # 90
You're working on a project and are working with personally identifiable information (PII). What's the best approach to take when it comes to collecting and using this data?

A. Use noise reduction techniques to reduce all forms of data noise
B. If this data is not needed, use Data anonymization techniques to remove it before feeding to models
C. Implement a new data privacy policy
D. Store the data in a data warehouse

Answer: B

Explanation:
Under CPMAI Phase III: Data Preparation, the Data Format task includes "Data anonymization" as a core activity to remove or mask PII when it is not required for modeling, thereby protecting privacy while retaining data utility.
=========

NEW QUESTION # 91
During CPMAI Phase IV: Model Development, which of the following is not done during this phase?

A. Model Selection
B. Model tuning
C. Algorithm Selection
D. Model training

Answer: A

Explanation:
The Phase IV: Model Development generic tasks include:
Select Modeling Technique (algorithm selection)
Generate model test design
Model Training / Model Building
Hyperparameter Optimization (model tuning)
Final Model Selection (choosing the best candidate against business criteria) is performed in Phase V: Model Evaluation, not in Phase IV .
=========

NEW QUESTION # 92
One of the key elements of a data-centric methodology is the data requirements phase. During CPMAI Phase II, several unexpected issues have developed and are now threatening the data collection efforts.
What course of action might make the issue worse?

A. See if you can adjust the scope of this interaction to continue with the project
B. See if you already have access to enough data to continue with the project
C. See if you can expand the scope to continue with the project
D. See if you can purchase the data needed to continue with the project

Answer: C

Explanation:
In Phase II: Data Understanding, CPMAI urges teams to rigorously assess data feasibility-asking whether the data is available, sufficient in quality, and properly aligned with business goals-and to perform a Go/No- Go decision before proceeding . Expanding project scope in the face of data issues violates the methodology's iterative, scope-controlled approach. Instead, CPMAI recommends either down-scoping (Option C), verifying existing data sufficiency (Option B), or identifying necessary data sources (Option D) to resolve issues without amplifying risk.

NEW QUESTION # 93
......

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